code: def __init__(self, inplanes, branch_planes, outplanes):
      super().__init__()
      self.scale1 = nn.Sequential(
          nn.AvgPool2D(kernel_size=5, stride=2, padding=2),
          layers.SyncBatchNorm(inplanes),
          nn.ReLU(),
          nn.Conv2D(
              inplanes, branch_planes, kernel_size=1, bias_attr=False
          ),
      )
      self.scale2 = nn.Sequential(
          nn.AvgPool2D(kernel_size=9, stride=4, padding=4),
          layers.SyncBatchNorm(inplanes),
          nn.ReLU(),
          nn.Conv2D(
              inplanes, branch_planes, kernel_size=1, bias_attr=False
          ),
      )
      self.scale3 = nn.Sequential(
          nn.AvgPool2D(kernel_size=17, stride=8, padding=8),
          layers.SyncBatchNorm(inplanes),
          nn.ReLU(),
          nn.Conv2D(
              inplanes, branch_planes, kernel_size=1, bias_attr=False
          ),
      )
      self.scale4 = nn.Sequential(
          nn.AdaptiveAvgPool2D((1, 1)),
          layers.SyncBatchNorm(inplanes),
          nn.ReLU(),
          nn.Conv2D(
              inplanes, branch_planes, kernel_size=1, bias_attr=False
          ),
      )
      self.scale0 = nn.Sequential(
          layers.SyncBatchNorm(inplanes),
          nn.ReLU(),
          nn.Conv2D(
              inplanes, branch_planes, kernel_size=1, bias_attr=False
          ),
      )
      self.process1 = nn.Sequential(
          layers.SyncBatchNorm(branch_planes),
          nn.ReLU(),
          nn.Conv2D(
              branch_planes,
              branch_planes,
              kernel_size=3,
              padding=1,
              bias_attr=False,
          ),
      )
      self.process2 = nn.Sequential(
          layers.SyncBatchNorm(branch_planes),
          nn.ReLU(),
          nn.Conv2D(
              branch_planes,
              branch_planes,
              kernel_size=3,
              padding=1,
              bias_attr=False,
          ),
      )
      self.process3 = nn.Sequential(
          layers.SyncBatchNorm(branch_planes),
          nn.ReLU(),
          nn.Conv2D(
              branch_planes,
              branch_planes,
              kernel_size=3,
              padding=1,
              bias_attr=False,
          ),
      )
      self.process4 = nn.Sequential(
          layers.SyncBatchNorm(branch_planes),
          nn.ReLU(),
          nn.Conv2D(
              branch_planes,
              branch_planes,
              kernel_size=3,
              padding=1,
              bias_attr=False,
          ),
      )
      self.compression = nn.Sequential(
          layers.SyncBatchNorm(branch_planes * 5),
          nn.ReLU(),
          nn.Conv2D(
              branch_planes * 5,
              outplanes,
              kernel_size=1,
              bias_attr=False,
          ),
      )
      self.shortcut = nn.Sequential(
          layers.SyncBatchNorm(inplanes),
          nn.ReLU(),
          nn.Conv2D(
              inplanes, outplanes, kernel_size=1, bias_attr=False
          ),
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new DAPPM object from inherited base classes: nn with example: obj = DAPPM(inplanes, branch_planes, outplanes)